Applied AI Engineer — Build AI Tools & Production Systems

remote work; United States; Hybrid; USD 110,000 - 150,000; Full time

Benefits offered by this job

Relocation support

Flexible work

Healthcare Coverage

Education Budget

Wellness Budget

Vacation Policy

Job summary

Applied AI Engineers are invited to join a flexible, US-based company focused on turning business problems into working software. You will build production AI systems across teams, including customer-facing assistants and internal automation, with cross-functional collaboration across backend, product, and business stakeholders.

You will shape evaluation methods, run experiments, and own end-to-end LLM pipelines from model selection to release and monitoring.

Qualifications

• Experience in a Machine Learning role
• Experience with agent frameworks or tool-using LLM systems
• Experience designing evaluations for LLM systems - representative task sets, automated graders, production signals, and human review
• Experience developing and launching AI products or features from scratch as an AI/ML/DS engineer
• High autonomy and ownership

Responsibilities

• Build AI-powered products such as assistants, chatbots, copilots, and internal automation
• Improve model quality where it matters - retrieval, prompting, fine-tuning, and model selection
• Own LLM pipelines end-to-end from design to release and monitoring
• Investigate and resolve production issues
• Create data loops turning real usage into evaluation datasets
• Design evaluation methodologies for AI systems and measure 'good' outcomes
• Run experiments and A/B tests to validate improvements
• Collaborate with engineers and business owners to ship model-derived improvements

Skills

Machine Learning

LLM Systems

Evaluations for LLM

AI Product Development

Big-picture thinking

Autonomy

English communication

Job description

Applied AI Engineers are invited to join a flexible, US-based company focused on turning business problems into working software. You will build production AI systems across teams, including customer-facing assistants and internal automation, with cross-functional collaboration across backend, product, and business stakeholders.

You will shape evaluation methods, run experiments, and own end-to-end LLM pipelines from model selection to release and monitoring.